76,424 research outputs found
Mobile, collaborative augmented reality using cloudlets
The evolution in mobile applications to support advanced interactivity and demanding multimedia features is still ongoing. Novel application concepts (e.g. mobile Augmented Reality (AR)) are however hindered by the inherently limited resources available on mobile platforms (not withstanding the dramatic performance increases of mobile hardware). Offloading resource intensive application components to the cloud, also known as "cyber foraging", has proven to be a valuable solution in a variety of scenarios. However, also for collaborative scenarios, in which data together with its processing are shared between multiple users, this offloading concept is highly promising. In this paper, we investigate the challenges posed by offloading collaborative mobile applications. We present a middleware platform capable of autonomously deploying software components to minimize average CPU load, while guaranteeing smooth collaboration. As a use case, we present and evaluate a collaborative AR application, offering interaction between users, the physical environment as well as with the virtual objects superimposed on this physical environment
Scalable architectures for platform-as-a-service clouds: performance and cost analysis
Scalability is a significant feature of cloud computing, which ad-dresses to increase or decrease the capacities of allocated virtual resources at application, platform, database and infrastructure level on demand. We investigate scalable architecture solutions for cloud PaaS that allow services to utilize the resources dynamically and effectively without directly affecting users. We have implemented scalable architectures with different session state management solutions, deploying an online shopping cart application in a PaaS solution, and measuring the performance and cost under three server-side session state providers: Caching, SQL database and NoSQL database. A commercial solution with its supporting state management components has been used. Particularly when re-architecting software for the cloud, the trade-off between performance, scalability and cost implications needs to be discussed
A DevOps approach to integration of software components in an EU research project
We present a description of the development and deployment infrastructure being created to support the integration effort of HARNESS, an EU FP7 project. HARNESS is a multi-partner research project intended to bring the power of heterogeneous resources to the cloud. It consists of a number of different services and technologies that interact with the OpenStack cloud computing platform at various levels. Many of these components are being developed independently by different teams at different locations across Europe, and keeping the work fully integrated is a challenge. We use a combination of Vagrant based virtual machines, Docker containers, and Ansible playbooks to provide a consistent and up-to-date environment to each developer. The same playbooks used to configure local virtual machines are also used to manage a static testbed with heterogeneous compute and storage devices, and to automate ephemeral larger-scale deployments to Grid5000. Access to internal projects is managed by GitLab, and automated testing of services within Docker-based environments and integrated deployments within virtual-machines is provided by Buildbot
Deploying AI Frameworks on Secure HPC Systems with Containers
The increasing interest in the usage of Artificial Intelligence techniques
(AI) from the research community and industry to tackle "real world" problems,
requires High Performance Computing (HPC) resources to efficiently compute and
scale complex algorithms across thousands of nodes. Unfortunately, typical data
scientists are not familiar with the unique requirements and characteristics of
HPC environments. They usually develop their applications with high-level
scripting languages or frameworks such as TensorFlow and the installation
process often requires connection to external systems to download open source
software during the build. HPC environments, on the other hand, are often based
on closed source applications that incorporate parallel and distributed
computing API's such as MPI and OpenMP, while users have restricted
administrator privileges, and face security restrictions such as not allowing
access to external systems. In this paper we discuss the issues associated with
the deployment of AI frameworks in a secure HPC environment and how we
successfully deploy AI frameworks on SuperMUC-NG with Charliecloud.Comment: 6 pages, 2 figures, 2019 IEEE High Performance Extreme Computing
Conferenc
Cloudbus Toolkit for Market-Oriented Cloud Computing
This keynote paper: (1) presents the 21st century vision of computing and
identifies various IT paradigms promising to deliver computing as a utility;
(2) defines the architecture for creating market-oriented Clouds and computing
atmosphere by leveraging technologies such as virtual machines; (3) provides
thoughts on market-based resource management strategies that encompass both
customer-driven service management and computational risk management to sustain
SLA-oriented resource allocation; (4) presents the work carried out as part of
our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a
Service software system containing SDK (Software Development Kit) for
construction of Cloud applications and deployment on private or public Clouds,
in addition to supporting market-oriented resource management; (ii)
internetworking of Clouds for dynamic creation of federated computing
environments for scaling of elastic applications; (iii) creation of 3rd party
Cloud brokering services for building content delivery networks and e-Science
applications and their deployment on capabilities of IaaS providers such as
Amazon along with Grid mashups; (iv) CloudSim supporting modelling and
simulation of Clouds for performance studies; (v) Energy Efficient Resource
Allocation Mechanisms and Techniques for creation and management of Green
Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape
Software Provision Process for EGI
he European Grid Initiative (EGI) provides a sustainable pan-European Grid computing infrastructure for e-Science based on a network of regional and national Grids. The middleware driving this production infrastructure is constantly adapted to the changing needs of the EGI Community by deploying new features and phasing out other features and components that are no longer needed. Unlike previous e-Infrastructure projects, EGI does not develop its own middleware solution, but instead sources the required components from Technology Providers and integrates them in the Unified Middleware Distribution (UMD). In order to guarantee a high quality and reliable operation of the infrastructure, all UMD software must undergo a release process that covers the definition of the functional, performance and quality requirements, the verification of those requirements and testing in production environments
Deploying Jupyter Notebooks at scale on XSEDE resources for Science Gateways and workshops
Jupyter Notebooks have become a mainstream tool for interactive computing in
every field of science. Jupyter Notebooks are suitable as companion
applications for Science Gateways, providing more flexibility and
post-processing capability to the users. Moreover they are often used in
training events and workshops to provide immediate access to a pre-configured
interactive computing environment. The Jupyter team released the JupyterHub web
application to provide a platform where multiple users can login and access a
Jupyter Notebook environment. When the number of users and memory requirements
are low, it is easy to setup JupyterHub on a single server. However, setup
becomes more complicated when we need to serve Jupyter Notebooks at scale to
tens or hundreds of users. In this paper we will present three strategies for
deploying JupyterHub at scale on XSEDE resources. All options share the
deployment of JupyterHub on a Virtual Machine on XSEDE Jetstream. In the first
scenario, JupyterHub connects to a supercomputer and launches a single node job
on behalf of each user and proxies back the Notebook from the computing node
back to the user's browser. In the second scenario, implemented in the context
of a XSEDE consultation for the IRIS consortium for Seismology, we deploy
Docker in Swarm mode to coordinate many XSEDE Jetstream virtual machines to
provide Notebooks with persistent storage and quota. In the last scenario we
install the Kubernetes containers orchestration framework on Jetstream to
provide a fault-tolerant JupyterHub deployment with a distributed filesystem
and capability to scale to thousands of users. In the conclusion section we
provide a link to step-by-step tutorials complete with all the necessary
commands and configuration files to replicate these deployments.Comment: 7 pages, 3 figures, PEARC '18: Practice and Experience in Advanced
Research Computing, July 22--26, 2018, Pittsburgh, PA, US
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